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Secure third-party data clustering using SecureCL, φ-data and multi-user order preserving encryption
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DOI：10.1111/exsy.12581.png)
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Secure collaborative data clustering using SecureCL is presented. SecureCL is founded on the concept of phi-data implemented using Super Secure Chain Distance Matrices and encrypted using Multi-User Order Preserving Encryption. The advantage offered, unlike comparable systems, is that SecureCL does not require any user participation once the phi-data proxy has been encrypted; it does not require recourse to Secure Multi-Party Computation protocols or 'secret sharing' mechanisms. The utility of SecureCL is illustrated using Nearest Neighbour Clustering and Density-Based Spatial Clustering of Applications with Noise, although it can be applied to any data clustering algorithm that involves distance comparison. The reported experiments demonstrate that SecureCL can produce securely cluster configurations comparable to those produced using standard, non-encrypted, approaches without entailing any significant computational overhead, thus indicating its suitability in the context of Data Mining as a Service.
Keyword:
cryptography
data mining
nearest neighbour
privacy
unsupervised learning
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